Christopher W. Anderson

Transcription

1 prepared for the 1st Berlin Symposium on Internet and Society October 26th 28th 2011 Understanding the Role Played by Algorithms and Computational Practices in the Collection, Evaluation, Presentation, and Dissemination of Journalistic Evidence Conference Draft Christopher W. Anderson

2 Paper prepared for the 1st Berlin Symposium on Internet and Society, Oct , 2011 Understanding the Role Played By Algorithms and Computational Practices in the Collection, Evaluation, Presentation, and Dissemination of Journalistic Evidence C.W. Anderson The College of Staten Island (City University of New York), Department of Media Culture Victory Boulevard. Staten Island, NY ABSTRACT: This article advances a critical research approach to computational journalism. By computational journalism the article refers to the increasingly ubiquitous forms of algorithmic, social scientific, and mathematical forms of newswork adopted by many 21 st century newsrooms and touted by many educational institutions as the future of news. The bulk of the article outlines a series of six lenses through which such a critical approach to computational journalism might be carried out. Four of these lenses are drawn from Schudson s classic typology of the sociology of news economic, political, cultural, and organizational approaches. In addition, the author adds Bordieuean field approaches and technological lenses to the mix. In each instance, the author discusses how particular approaches might need to be modified in order to study computational journalism in the digital age. 1

3 Introduction In 2007, the news and financial market data provider Reuters (now merged with information and technology firm Thompson Company) purchased the text search firm ClearForest, a maker of software used to search vast archives of news, Web pages and documents for relevant facts (Auchard 2007) In the spring of 2008, Thompson Reuters launched the Calais web service, a web service that: automatically attaches rich semantic metadata to the content you submit. Using natural language processing, machine learning and other methods, Calais categorizes and links your document with entities (people, places, organizations, etc.), facts (person "x" works for company "y"), and events (person "z" was appointed chairman of company "y" on date "x") ( Calais Viewer ). Calais, in short, automatically extracts facts from news (and other kinds of) narratives. In the summer of 2009, a related service, called MemeTracker, made its debit. MemeTracker builds maps of the daily news cycle by track[ing] the quotes and phrases that appear most frequently over time across this entire online news spectrum. This makes it possible to see how different stories compete for news and blog coverage each day, and how certain stories persist while others fade quickly. Like Open Calais, the power of MemeTracker lies in its ability to extract nuggets of news ( memes ) from the swirl of narrative text and, over time, build connections and dynamic maps of these news nuggets. In August of 2011, the venerable Google News supplemented its entirely algorithmically controlled from page with human-guided editor s picks, featuring the news judgments of real, live human editors. As Megan Garber with the Nieman Journalism Lab noted: When Google News launched in 2002, it did so with some declarations: This page was generated entirely by computer algorithms without human editors. And: No humans were harmed or even used in the creation of this page. But the thing about humans is that, occasionally, they re helpful to have around. Especially when it comes to the increasingly difficult task that is keeping track of the world as it twists and turns. Which is why, starting today, Google News is introducing a new section to its U.S. edition: Editors Picks, a display of original content that journalists (human ones!) have selected as editorial highlights from their publications. (Garber 2011) 2

4 The resulting debate over the role of human-editor hybrids in the new media ecosystem sheds light on the processes by which news is selected, filtered, and edited in the 21 st century. Perhaps the most the interesting thing about all these examples-- Open Calais, MemeTracker, and Google News-- is that they begin turn our focus to the objects of information, including the objects of news. By objects of news I mean the fragments of information, data, and technology that are pieced together into various news stories. By objects of news I also mean news these news stories themselves, and the manner in which they circulate inside larger news ecosystems. The purpose of this working paper is to consider the algorithm as a new object of news that intersects with both journalistic practices and products, and ultimately affects the very definition of journalism itself. The second section of the paper discusses some possible ways in which communication scholars might want to approach the study of algorithms as journalistic objects. Embedded within this second section are a series of possible lenses for future research, which are addressed specifically as future research questions in the third and final section. Objects of Evidence in Journalism: Thinking About Networked Things Objects of news have always been part and parcel of the practice and study of journalism though they have rarely been discussed in precisely these terms. For instance, in sources as diverse as an introductory lecture in basic news reporting and writing, (fieldwork, November 2007), an online syllabus for budding citizen journalist (McGill n d), a guide for elementary school students about the nature of news (Niles n d), and a faculty handbook for the assessment of journalism and mass communication programs, writers make it clear that some of the most important objects of news include observations, interviews, and documents (McGill, n d). There are three main ways to gather information for a news story or opinion piece, writes the guide to journalism for children: Interviews: talking with people who know something about the story you are reporting. Observation: watching and listening where news is taking place. documents: Reading stories, reports, public records and other printed material (Niles n d). For budding 3

5 journalists on college campuses, the instructions are similar. A rubric for assessing the core competencies and learning objectives for journalism students lists the very first basic skill as gathering information through interviewing, observation, and the use of documents, printed and computerized, to write fair and balanced news stories. In these examples, documents, interviews, and observations each act as objects of news: lower-level fragments of news stories, forms of valid journalistic evidence (i.e., carriers of particular truth claims), and aspects of the reporting processes that have their own histories and their own ways of being integrated into larger processes of newswork. When we start thinking about documents, sources, and direct interviews as news objects-- objects that present particular evidentiary claims and are embedded in specific historical trajectories-- we can see that there might be additional objects that also constitute fragments of journalistic evidence, but which are less universally discussed by either journalism educators or journalism scholars. The spectrum of possible news objects, in other words, is far more complex than the usual rundown of documents, interviews, and observations might suggest, and might include public forums, links, databases, web metric reports, T-1 internet lines, and tweets as potential fragments of a larger journalistic network. It might also include algorithms. I now want to turn to an overview of the relationship between journalistic practices, journalistic products, and one particular journalistic object the algorithm-- along with some preliminary discussions of ways that we might want to study the computational journalistic practices that cluster around this object. What, exactly, is an algorithm? What could is possibly have to do with the way 21 st century journalism is practiced and understood? And how does it relate to practices of computational journalism more broadly? Use of the term algorithm dates back to at least the Middle Ages, and was originally used to distinguish scholars who utilized written, columnar calculation procedures from those performed calculations on an abacus (Stone 1972). In general, while there is no standard definition of algorithm, we can say that is constitutes a series of rules for accomplishing a particular task in a certain number of discrete steps. It is easy to see that, although algorithms were invented long before computers and other electronic calculation devices, the focus on 4

6 particular rules and repeatable steps would lead algorithms to a position of great prominence in the information age. In their simplest form, computers are devices that follow discrete rules and procedures in a rapidly repeatable fashion; they thus stand quite close to the algorithm in many respects. Perhaps the most famous algorithm in recent times is the Google PageRank algorithm, which dominates the 21 st century world of information retrieval. In the most basic sense, PageRank measures the relevance of a website by calculating the number of links to that site, and following this citation trail backwards in a series of increasingly complex and data-intensive steps. Google then uses this ranking to order its search results, thus bequeathing sites at the top of the results a tremendous amount of power, authority (and, it muse be said, money. While it can be said that Google ranks these sites, it is more accurate to say that the socio-technical network that is Google, as mediated through its proprietary, socio-technical PageRank algorithm, ranks these sites. Journalists are only beginning to think about how algorithms might be used to manage their own informational workflows in a manner similar to that by which Google stores, retrieves, and ranks digital information on the entire web. Understanding the relationship between algorithmic objects and journalistic practices, then, is ultimately a subset of a larger research question: understanding the interaction between large-scale data collection, algorithmic analysis, computational practices, and the production of public knowledge. I would argue that relationship between big data and communicative processes (such as computational journalism) has quickly emerged one of the central scholarly and methodological challenges of our time (Manovich 2011). If this is correct, than we need to find ways to bring old mechanisms of analyzing journalism to bear on these new computational practices and problems, including the problems of algorithmic journalism. The kind of analysis I advocate for the study of algorithms and computational journalism here would pay careful attention to the means by which public policies and struggles over the transparency of open-government data impinge on newswork. It would examine, in detail, the unequal distribution of computational resources in 21 st century journalism and critically dissect how this inequality is either impeding or facilitating journalism s professional mission. It would look 5

7 at the dynamics of the journalistic field and the manner in which struggles by entities outside that field are impacting the diffusion of computational practices. It would pay careful attention to the organizational dynamics and work processes that either facilitate, or hinder the adoption of computational techniques inside the newsroom. It would engage in a careful, historically grounded analysis of journalistic culture and the role that culture plays in our understanding of what counts as news. It would, finally, bring a nuanced, socio-material perspective on technology to bear on questions regarding the relationship between computational technology and journalistic sense-making. Astute readers will have noted note that four of the lenses I advocate here political, economic, organizational, and cultural frames have long been considered to be the classic perspectives within the sociology of news (Schudson 2005) while the other two technological and institutional, or field, perspectives though not classic, are increasingly utilized in 21 st century journalism research. I would also argue that two new additional frames may also be helpful in understanding journalism in an increasingly trans-institutional, technologically mediated age. These are not the only scholarly research perspectives on journalism, of course, but they are helpfully compact, and schematic enough to use as a starting point for analysis. The purpose of this categorical overview, in short, is to both examine the possible lenses through which to analyze a major new example of digital knowledge production in the 21 st century, and to highlight some of the early work that has already been done in this vein. In order to accomplish this, let s turn to a more in-depth discussion of these six categories and the way they might be applied to news algorithms and other computational practices. Six Approaches to the Sociology of Algorithms and Computational Journalism Michael Schudson s Sociology of News Production, first published in Media, Culture, and Society in 1989 and revised several times since then, remains one of the key citations in any sociological analysis of journalism and news. The article has actually undergone several important mutations over the course of its 20-plus year history, with the key changes being 6

8 outlined rather frankly in the latest incarnation of the piece ( Four Approaches to the Sociology of News, published in the equally oft-revised Mass Media and Society [5 th edition]). The different descriptions of each of the categories amount to more than simply revisions for the sake of revision, particularly in the latest piece, which signals its intentions by announcing the approaches to studying news have now expanded from three to four. The relevant nuances of these category changes will be explored below. An equally important change in Schudson s thinking the argument that journalistic products are as real as they are socially constructed lies outside the scope of this current paper, but would be valuable to analyze in its own right. What, then, are the different lenses though which we can analyze the sociology of news production, and how can each of them be applied to the study of computational journalism? Politics and Public Policy In its simplest form, the political context of news-making can be used to study the production of news on a macro or state-level, particularly insofar as different political systems coproduce different systems and styles of journalism. Political approaches to the study of news usually emphasize one or two unique, meta-theoretical points. First, the approach emphasizes that journalism is not co-terminous with democracy; contra Carey (Carey 2000), undemocratic states may possess their own, unique journalistic forms. Second and more importantly, the political approach to the analysis of journalism has been especially useful in the comparative study of news practices, particularly the different practices that exist within different democratic systems. Beginning with Hallin and Mancini s 2004 Comparing Media Systems (Hallin and Mancini 2004) a growing and persuasive body of research has pointed to variations within capitalist democracies (between France, Great Britain, and Germany, for instance) as being particularly important causal factors accounting for differences in news (Benson 2010; Hallin 2004; Hanitzsch 2008; Waisbord 2000) In part, this is a reaction to earlier generations of news research that tended to posit democratic, capitalistic journalistic systems as structurally 7

9 homogenous when compared to the journalism of socialist or communist nation-states (Siebert et. al 1956). At first glance, it is difficult to see how this political perspective might be at all useful in the analysis of algorithms and computational journalism. Is computational journalism not a microlevel or (at best) an organization-level phenomenon? How can it be studied in its political context without losing sight of what makes it unique? There is some truth to this claim. On the other hand, it is precisely by trying to fit the development of computational journalism within a macro-political frame that our attention is drawn to aspects of it that we might otherwise neglect. When analyzing computational journalism through a political lens, scholarship should initially focus less on comparative differences in political structure and more on the bureaucratic, policy-level initiatives that either allow computational journalism to thrive, or retard its growth. Developments in the world of so-called open government initiatives the role of large-scale databases in the crafting of public policy, the transparency and accessibility of government data and its use in democratic decision-making, government surveillance of online data and political activities; all these statelevel initiatives may ultimately influence the forms of computational, data-driven journalism that news-organizations adopt. In general, work on internet-afforded public policy looks at one of two phenomena either digitally enhanced citizen-participation (Noveck 2009) or the more passive activity of making government data easily accessible and searchable via digital archives. While open and wiki government initiatives have received much attention from policy-makers, activists, and reporters, the scholarly community has yet to devote much attention to these phenomena. Noveck (2011) has been an exception, devoting the last several years to both open government policy-making in Washington D.C as a member of the Obama administration as well as academic research. Her work, however, spends little time discussing the relationship between wikigovernment and journalism, and has little to say about news. One of the few academic pieces to directly tie developments in digital transparency to journalistic practices comes (again) from Schudson, who argues that the growth of what he calls political observatories and the new availability of databases for public-interest research 8

10 represent just one feature of the future for news. Yet it is a vital feature, and so far it has received little general notice. Political observatories do not replace journalists, nor do databases shove narratives aside. But the observatories are increasingly valuable partners for journalists, and databases lay new foundations for narrative. Both offer promise for developing the kind of public information that makes democracy possible. (Schudson 2010, 8) Of course, the flipside to digitally afforded public transparency is digitally-enabled surveillance, as Kreiss and Howard remind us (Kreiss and Howard 2010). But it is this complex overlap, embedded within a deep and nuanced understanding of that journalism s political context, which a critical analysis of computational journalism would attempt to understand. The public policy decisions that enable or retard the growth of computational journalism are surely a ripe area for research. Ultimately, given a lengthy enough amount of time, scholars might even be equipped to relate differences in computational practices to system-level differences between a variety of political contexts. Economics Inside in a large conference room on the upper-floor of a center city office building, several employees were grilling the Vice-President of Philly.com, the news website for Philadelphia s two daily newspapers, on her hiring priorities. The year was 2008, and the staff of Philly.com and the newspapers had gathered for an afternoon to plot their future. You ve been hiring all these web producers, one reporter complained, these kids who just take newspaper content and move it around Philly.com. But what it really seems we need are developers, computer programmers, silicon-valley types. Why aren t we hiring them? 1 There was a pause and some uncomfortable throat clearing. Well, the Philly.com Vice- President finally ventured, the problem, you see, is that the web producers I hire are cheap. But the tech team you and I both know we need to hire? They aren t cheap. I can hire six or seven 1 The following anecdote is taken from the Authors s newsroom fieldwork, conducted between 2007 and

11 web producers for what it costs to hire a single tech person. And we really just can t afford to do that. Traditionally the analysis of the economic organization of news, like the analysis of the political context of news making, has been considered on a macro-level. With its origins in both Cold War dynamics (the division of the world into socialist and capitalist economic systems) and the political-economy critique of concentrated media systems, the economic analysis of news production (Baker 2002; McChesney 1995; Siebert 1956) might seem a poor way of understanding computational journalism. But just as in the political lens discussed above, drawing on economic categories to understand the development big data journalism might point our analysis in directions we might not immediately expect. Rather than simply focusing on the manner in which large economic systems intersect with various forms of journalistic production, scholars might critically interrogate the way that different institutionally specific resources constrain the options available to various news outlets and industry segments. Such scholarship could attempt to correlate forms of computationally enhanced news production with levels of institutional economic capital. Researchers in this tradition would take seriously the lament of the local newspaper executive, above, the fact that certain technologically-focused innovations appeared out of reach for less wealthy news organizations, and they would interrogate that lament by examining the role played by those seeking to level the playing field-- open-source software makers and volunteers-- in the more evenly spread diffusion of technology. To date, there has been next to no work done on the relationship between economic resources and computational journalism. Nevertheless, white-papers like the recently released FCC report on the Information Needs of Communities, with its emphasis on capacity gaps between local and nationally focused news organizations, can serve as a starting point for a less macro-level, more institutionally grounded analysis (Waldman 2011). Of course, a retreat from systemic economic perspectives does not obviate the feasibility of a systemic analytical lens over the long term; like the political context lens, drawing stronger and more generalizable correlations between economic system and journalistic form might be possible over time. In the interim, 10

12 however, such correlations would require extensive, critically focused empirical work on the relationship between economics and newsroom innovation. Institutions and Fields The shifting focus in the political and economic perspectives discussed above, away from macro-level generalizations and towards institutionally-grounded analysis, points us toward a third lens that might be useful in understanding computational journalism: the so-called field or institutional perspective. Schudson does not discuss field analysis in his classic series of papers on the sociology of news; indeed, Rodney Benson, who has led the way in importing field perspectives into journalism research, advances mezzo-institutional field research as part of a critique of classic sociological lenses on the production of news, including Schudson s (Benson 1999; Benson 2004). I do not attempt to take sides in this dispute here, except insofar as I argue that any one of the three scholarly lenses discussed so far political, economic, and field may be useful at different times and insofar as they attempt to answer different questions. As a new object of study, scholarship on computational journalism would be best served by openness to a variety of methods and theories. And understanding the development of computational journalism from the perspective of the journalistic field has its own advantages, as well as its own drawbacks. Drawing on Bourdieu s highly influential work on the sociology of institutional power and applying it to the study of news, Benson argues that The starting point for understanding the media field paradigm is Pierre Bourdieu's general theory of fields (champs). Drawing on and modifying Weber's sociology of religion, Bourdieu sees society as differentiated into a number of semi-autonomous fields (e.g., fields of politics, economics, religion, cultural production, etc.) governed by their own "rules of the game" and offering their own particular economy of exchange and reward, yet whose basic oppositions and general structures parallel each other. (Benson 1999, 466) Journalism, according to this model, would best be understood as its own field of power whose general social dynamics are conceptualized as positional and relational. These dynamics 11

13 are based on the possession of a particular habitus as well as a stock of capital. Institutions (and individuals) would struggle to possess certain levels of socially-defined capital, as well as to alter the definition of what counts as legitimate capital in the first place. Understanding computational journalism in this fashion has at least two benefits. First, it adds a vector of power dynamics to an area of socio-technical life (technological innovation) too often understood from within an all boats will rise mentality. Second, and perhaps more importantly, it understands journalism relationally, placing it in the context of other institutional clusters and fields. This analysis of computational journalism from a Bourdieuean or new institutionalist (Ryfe 2006) perspective would begin from the premise that a new field, the field of data journalism, is emerging alongside traditional fields such as computer science and news reporting. It would study the social, economic, and cultural power struggles that occur inside this field, and the way these struggles shape practices of newswork and journalistic products. Importantly, however, the dynamics of this field would never be seen as operating in isolation from other nearby fields. Developments in traditional journalism, or in computer science, would obviously play key roles in the development of data journalism. And clusters of seemingly unrelated institutions, like non-forprofit foundations, would also play a key role in the shape of the computational journalistic field. Lewis work analyzing the relationship between the Knight Foundation, news institutions, and cultural concepts of journalistic professionalism (Lewis 2011) is a particularly impressive example of this kind of research; similar methodologies and perspectives might be applied to the study of the role of the Knight Foundation, open source technology groups like the Mozilla Foundation, and other funders in the development of the computational journalistic field. Organization-Level Dynamics Despite the utility of political, economic, and field approaches to the study of computational journalism, each of these perspectives abstracts (to a greater or lesser degree) from the day-to-day organizational processes by which computational practices embed themselves in journalistic work. A fourth perspective, the social organization of newswork, 12

14 (Schudson 2005) takes us back down to earth, to ground-level newsroom dynamics. Research operating via this lens would examine how workflow routines, levels of technological adaptation, individual rivalries, bureaucratic divisions, and daily process imperatives affect the manner in which newsrooms integrate potentially far-reaching new technologies into their workflow. The manner by which news organizations incorporate powerful algorithms into daily news practices is only one example of this type of approach. While earlier research of the impact of technology on journalistic practice often posited a dynamic in which new technologies generate distinct editorial effects, Boczkowski (2004) has provided researchers with a far more nuanced model by which to understand the relationship between technology and newswork. For Boczkowski, production factors (organizational factors, work routines, and representations of users) shape adoption processes, which are themselves afforded by technological changes. Only at the end of this far more complex process do distinct editorial products begin to emerge. Most newsroom ethnographies either explicitly or implicitly adopt an organizational routines perspective, with detail-rich, on the ground observations often bringing to light just how slow an halting organizational change actually is. Alongside classic work in this vein (Epstein 2000; Gans 1979; Tuchman 1978), the last decade has witnessed a veritable outpouring of ethnographic newsroom research (Anderson 2010a; Boczkowski, 2004a; Cottle, 2007; Eliasoph, 1997; Klinenberg, 2005; Paterson and Domingo, 2011) research often prompted by macro-level technological change but whose conclusions have often been that ground-level journalistic changes are far less dramatic than might be assumed. And while much of this research has looked at technology in general the use of crowdsourcing, blogs, Twitter, etc little of it has explored computational journalism, if by computational journalism we mean (as I argued above) the combination of algorithms, data, and knowledge from the social sciences to supplement the accountability function of journalism (Turner 2009). Royal s provisional research, which analyzed the organizational integration of the New York Times Interactive New Department into larger work routines at the Times, is one of the few exceptions (Royal 2010). There is much room for additional scholarship in this area. Not only would it be easy to apply recently reinvigorated ethnographic methods to the study of computational practices, but it would be intriguing to see 13

15 whether differences existed in the findings of earlier and later digital newsroom ethnographies, and what these differences might say about journalism in a digital age. Cultural History The intersection of technological change and newsroom practice is most often analyzed on the level of organizational routines; nevertheless, the growth of big datasets and computational practices can be expected to affect journalistic culture as much as journalistic work. Schudson draws on Sahlins to argue that cultural aspects of society, while they may be uncovered by detailed historical analysis, cannot be extrapolated from features of social organization at the moment of study. They are part of culture a given symbolic system within which and in relation to which reporters and officials go about their duties (Schudson 2005, 187). There is much debate about the role played by culture in the operation of journalism. Benson, referring to forms of culture grounded in national differences, argues that the term should be abandoned. It must be admitted that there is a slight lack of clarity in Schduson s discussion of the journalistic culture; he does occasionally refer to national cultures and the role they play in the construction of news national differences that, as Benson argues, may be more usefully be seen as the outcome of field dynamics rather than symbolic systems per se. Nevertheless, Schudson s arguments about culture and journalism are far subtler and far ranging than Benson allows. From the perspective of praxis, culture might be seen as the accrued, long-term detritus of daily newswork, a detritus that creates the symbolic background against which journalists guide their actions. Or we may see it as the background symbolic system that guides many of the most important and obvious but least understood aspects of journalistic practice. The culture of journalism is invoked by Schudson to guide researchers toward what I would call interesting but tricky problems : journalist s vague renderings of how they know news when they see it [why] news stories are so often personified [why] reporters write of persons and not structures, of individuals not social forces and [journalistic] assumptions about narrative, storytelling, human interest, and the conventions of photographic and linguistic presentation. (188-89). Obviously, all 14

16 these questions can be reoriented to shed light on the nuances of data journalism. Does using big data create a difference sense of what news is? Does data encourage a greater emphasis on structures and social forces as opposed to personalities and incidents? The analysis of organizational routines might begin to answer these questions, but newsroom sociology cannot definitively answer them. For Schudson, Zelizer (Zelizer 1992) Carey (Carey 2008), and others, only a cultural analysis informed by nuanced historical research can do the heavy lifting required to answer these questions. To date, most of the detailed historical research on the relationship between journalism s symbolic systems, computer programming, and big data remains to be done. While he might not call his research cultural history, Powers work on the evolution of journalistic computational talk from 1975 to the present day (Powers, forthcoming) is one possible exception; for Powers, discussions of computers and journalism usually frame these devices as providing continuity of work routines, threats to journalism, and opportunities for professional reform. Powers lengthy analytical time frame and his focus on value, technology, and work thus marks one model to be emulated by computational journalism scholarship. Research that embraces an even longer timeframe, such as Barnhurst and Nerone s analysis of the forms of news (Barnhurst and Nerone 2001) or my own work on news objects (Anderson, forthcoming) can also be seen as providing openings for the future analysis of the impact of big data on journalistic sense-making. Technology and the News In this paper, up until this point, technology has been discussed as a form of embedded materiality that intersects with journalism via the mediation of cultural, political economic, organizational, or institutional factors. To conclude, I want to advance the notion of technology as an independent lens of analysis without advocating, I hope, a pernicious form of technological determinism. For most sociology, an over-emphasis on the role played technology in the construction of news constitutes the primary sin, one to be assiduously avoided. But is there a way to talk about technology and the news on its own terms, without reducing said technology to 15

17 either a political, economic, cultural, or social construction? I would argue that, to a limited degree, there is. Some key aspects of computational journalism are themselves partly technological in nature; they are in part determined by the vast increase in computer processing speed and a growing data ubiquity, as seen over the last thirty years. These technological developments are not, in and of themselves the primary determinants of the shape computational journalism has taken in the past or may take in the future. I hope that each of the five lenses discussed so far has properly inoculated readers against visions of technologically guided progress; the meaning and impact of computational journalism is as much affected by politics, economics, institutions, culture, and organizational routines as it is by microchips and Moore s Law. Nevertheless, while not determinative, a scholarly concern with actual role played by materiality and technology in the processes of journalism might emphasize at least three areas: the oft-hidden intersection between imagined values and engineering design during the construction of journalistic artifacts, the increasingly hybrid nature of newsroom sorting and filtering technologies, and the changing status of journalistic evidence fostered by the exponential increase in available evidentiary forms in the digital age. To date, little serious work has been done in any of these areas, though extensive examples from field of science and technology studies provide examples of what such a research agenda might look like if applied to journalism (Collins and Evans 2002; Dunbar- Hester 2009; Latour and Woolgar 1976; Latour 2010; Law 1990; Pickering 2003).. In a technologically oriented study of computational journalism, researchers might first wish to probe the manner in which journalistic values refract through the material lens of digital design. Here, Nissenbaum s work on values in design (Nissenbaum 2004) might serve as a potential guide. In designing semantic tagging systems to assist Google and other search engines in the proper sorting of news content, how do engineers filter newsroom notions such as Authorship, the byline, and the fact though taxonomical systems originally designed for use in computer science? What aspects of the traditional story, in short, appear to be the most valued in the artifact building process, and what aspects are neglected? This focus on the manner by which human needs are embedded within and yet refracted through technological artifacts could draw 16

18 researchers attention to a second aspect of computational journalism: the hybrid nature of newsroom sense-making technologies. Data-crunching algorithms and other increasingly invisible information ordering devices are neither entirely material, nor are they entirely human they are hybrid, composed of both human intentionality and material obduracy. In this way, the human becomes partially obdurate and the material partly intentional (Latour 1993). Third and finally, the analysis of computational journalism through a technological lens would focus on the manner in which the explosion of digital objects - Tweets, links, blog posts, databases, etc changes journalists evidentiary calculations about what counts as proper story evidence. While none of these three angles amount to a methodological research prescription per se, a technological approach to computational journalism would be as much about asking particular types of research questions and emphasizing different areas of scholarly interest than the previously discussed lenses. Future Research Given the previously outlined argument that algorithms should be approached as potential news objects, as well as the claim that computational and big data journalism is one of the key research domains for journalism and communications studies in the future, what are some specific topics that media scholars might wish to investigate in this burgeoning area? Readers will note that a number of potential research topics have already been posed, in passing, in the sections above. I want to summarize four of these topics here, and conclude with some thoughts on the importance of cross-national, interdisciplinary research to the study of algorithms in the news, and to the study of computational journalism in general. I want to note that these research areas primarily serve as a starting point for future discussion and do not amount to a full-fledged research are per se. Potential items for future research include: 17

19 Transformations in Journalism Education and Newsroom Socialization: Given that journalists are now confronted with a series of new digital objects, sources of evidence, and methods of quantitative analysis, will the training of journalism (either formal or informal) change accordingly? How are journalism schools seeking to adapt their curricula to face these new empirical and methodological challenges? And can we compare these shifts in education and sociolization across time; for instance, to the rise of so-called precision journalism and computer-assisted reporting in the 1970s and 80s (Meyer 1991)? Comparative Analysis of Political System Transparency Initiatives and their Impact on Journalism: In the United States, there have been a number of recent governmentled initiatives to push for greater transparency of public information and government data (Schudson 2010). Obviously, however, public-sector attitudes towards transparency differ cross-nationally (see differing attitudes towards the Google Streetview program in the United States and Europe, just to name one recent example). And to the degree that public-sector data transparency affects journalism, different attitudes towards that transparency will affect journalism differently. Analysis of computational journalism as embedded within political systems, then, should be approached from a cross-national perspective as well. This line of analysis would obviously consider legal and regulatory questions as well. Computational News Routines: Scholarship on journalism in the intertnet age has begun to make considerable progress in understanding the manner by which larger changes in communicative systems (the so-called change from a one-to-many to a manyto-many informational system) are impacting newswork (see Singer et. al. 2011). Little research has yet been done, however, on the way that algorithms and computational practices are affecting news routines, individual rivalries, bureaucratic divisions, and daily process imperatives in newsrooms. Ethnographic research of this sort would go a long way to advancing our understanding of computational journalism. It would also be interesting to compare newser and older research. 18

20 Technologies and Tools Adapted by Journalists: How do journalists understand their own technological tools? What cultural meanings do they invest in algorithms, dataextraction analysis, and other objects of computational work? And do their understandings of what news is and the role it plays in society change as a result? This research would adopt the technological and culturalist perspectives on computational journalism outlined above. Such work would explore at least three areas: o the intersection between imagined values and engineering design during the construction of journalistic artifacts, o o the hybrid nature of newsroom sorting and filtering technologies, and the changing status of journalistic evidence fostered by the increase in evidentiary forms in the digital age. It should be obvious that research of the kind I outline here would require crossing borders in at least two important ways. First, this research would have to be cross-national. Indeed, given a research agenda that focuses so heavily on technology, cross-national research is the best guard against investing technology with deterministic properties. By watching how different organizational, political, regulatory, and economic systems refract technological processes, scholars can gain a more nuanced understanding of the impact of algorithmic and computational practices on journalism. Second, the research I advocate here would have to cross disciplinary borders as well as national ones. Given the wide variety of systems at play in the emergence of computational journalism, researchers would need to be drawn from fields of computer science, law and regulation, science and technology studies, and journalism studies. The rise of the algorithm inside newsrooms is a powerful development, one that requires an all hands on deck approach from scholars hoping to understand these dramatic changes in the way we live, work, communicate, and understand public issues in the 21 st century. Key Research Questions 19

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